Classification apparatus, classification method, and program

ABSTRACT

A classification apparatus includes: an encoding module that includes an element classification part that extracts a feature of input data and outputs classification information based on an element classification model stored in a first storage unit; an integration module that includes an element estimation part that receives the classification information and converts the classification information to a collation vector based on an element estimation model stored in a second storage unit; and a determination module that includes a determination part that determines a group to which the collation vector belongs by collating the collation vector with a representative vector of an individual group stored as a semantic model in a third storage unit and outputs a group ID of the group as a classification result.

REFERENCE TO RELATED APPLICATION

The present invention is based upon and claims the benefit of thepriority of Japanese patent application No. 2017-030272, filed on Feb.21, 2017, the disclosure of which is incorporated herein in its entiretyby reference thereto.

The present invention relates to a classification apparatus, aclassification method, and a program.

BACKGROUND

In many specialized domains, artificial intelligence (AI) technologycomparable to human capabilities is used. In image recognition usingdeep learning technology achieves a recognition rate equivalent to thatachieved by humans in a learned classification category. Factoryautomation has already been put to practical use and has greatlycontributed to improvement in productivity. AI in some specific domainshas already surpassed human capabilities. In a domain type where aprocedure is well defined and an answer is found by numeral calculation,advancement in computer has enabled AI to perform processing morequickly and accurately than humans. Factory automation and Go and Shogisoftware are examples of use of AI.

Currently, AI is a system that recognizes an event previously definedand acts as previously determined. Identifying in advance an event to berecognized, having AI learn the event, and accurately designing abehavior in advance are the most important. How uncertain elements areremoved affects the system performance. AI exhibits sufficientperformance in a predicable environment.

A related technique in which, for example, an external stimulus isrecognized (classified) is schematically illustrated in FIG. 1, forexample. This technique includes an encoding module 102 including afeature extraction part 1021 which receives input data 1011 from aninput apparatus 101 that acquires an external stimulus via a sensor notillustrated and outputs the input data 1011 as digital data, extracts afeature amount(s) of the input data 1011, and encodes the featureamount(s) as internal information (feature vector). The technique alsoincludes a determination module 103 including a matching part 1031 whichdetermines a group to which the external stimulus belongs bymatching(collating) the internal information from the encoding module102 and previously stored information and outputs a group identificationinformation (ID) 1032. The external stimulus may be image data acquiredby imaging means such as a camera or image data stored in an imagedatabase or the like.

As a related technology in which extracts a feature amount to performclustering, for example, PTL 1 discloses an image classificationapparatus which automatically classifies a plurality of items of imagedata stored in an image database into groups, each group having similarimages. This image classification apparatus includes: a feature amountcalculation part which calculates an overall feature amount for each ofa plurality of items of image data, detects an edge(s) of image data,and calculates a feature amount of the edge portion; a first clusteringpart which classifies the plurality of items of image data into aplurality of clusters based on the feature amount of overall images; asecond clustering part which further classifies the plurality ofclusters classified by the first clustering part into a plurality ofclusters based on the feature amount of the edge portion; and a clusterintegration part which determines, for each of the plurality of items ofimage data, pixels constituting a subject from a corresponding imagecomposition and integrates the plurality of clusters classified by thesecond clustering part based on the pixels constituting the respectivesubjects.

According to the related technology in FIG. 1, it is necessary to defineinformation about how the feature amount is encoded as internalinformation and classification groups in advance. There is also a knownmethod of manually designing a feature to be extracted and a group, andgenerating groups by using prepared learning data.

CITATION LIST Patent Literature

-   PTL 1: International Publication No. 2009/072466

SUMMARY Technical Problem

The related technology will be analyzed below.

Basically, the related technology described with reference to FIG. 1cannot be applied to external stimuli that are not defined in advance.Thus, a technique is desired that can determine, even when an unknownstimulus is inputted, a classification group to which the stimulusbelongs, while autonomously acquiring recognition and classificationgroups, for example, in FIG. 1.

The present invention has been made based on recognition of the aboveissues, and it is an object of the present invention to provide anapparatus, a method, and a program that can determine, even when anunknown stimulus is inputted, a classification group to which thestimulus belongs.

Solution to Problem

According to a mode of the present invention, there is provided aclassification apparatus (a developmental recognition apparatus)including: an encoding module which includes an element classificationpart that extracts a feature of input data and outputs classificationinformation based on an element classification model stored in a firststorage unit; an integration module which includes an element estimationpart that receives the classification information and converts theclassification information to a collation vector based on an elementestimation model stored in a second storage unit; and a determinationmodule which includes a determination part that determines a group towhich the collation vector belongs by collating the collation vectorwith a representative vector of an individual group stored as a semanticmodel in a third storage unit and outputs a group ID (Identification: widentification information) of the group as a classification result. Forexample, the input data may be digital data or the like obtained bysensing of an external stimulus performed by a sensor or the like.

According to a mode of the present invention, there is provided aclassification method (a developmental recognition method) performed bya computer, the classification method including:

an encoding step of extracting a feature of input data and outputtingclassification information based on an element classification modelstored in a first storage unit;

an integration step of receiving the classification information andconverts the classification information to a collation vector based onan element estimation model stored in a second storage unit; and

a determination step of determining a group to which the collationvector belongs by collating the collation vector with a representativevector of an individual group stored as a semantic model in a thirdstorage unit and outputting a group ID of the group as a classificationresult.

According to a mode of the present invention, there is provided aprogram (a developmental recognition program) causing a computer toexecute:

an encoding process including an element classification process thatextracts a feature of input data and outputting classificationinformation based on an element classification model stored in a firststorage unit;

an integration process that includes an element estimation process thatreceives the classification information and converts the classificationinformation to a collation vector based on an element estimation modelstored in a second storage unit; and

a determination process that determines a group to which the collationvector belongs by collating the collation vector with a representativevector of an individual group stored as a semantic model in a thirdstorage unit and outputs a group ID of the group as a classificationresult.

According to the present invention, there is provided a non-transitorycomputer-readable recording medium holding the above program (forexample, a semiconductor storage (such a random access memory (RAM), aread-only memory (ROM), or an electrically erasable and programmable ROM(EEPROM)), a hard disk drive (HDD), a compact disc (CD), or a digitalversatile disc (DVD)).

Advantageous Effects of Invention

According to the present invention, even when an unknown stimulus isinputted, a classification group to which the stimulus belongs can bedetermined.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating a related technology.

FIG. 2 is a diagram illustrating a configuration example according to afirst example embodiment.

FIG. 3A is a diagram illustrating the first example embodiment.

FIG. 3B is a diagram illustrating the first example embodiment.

FIG. 3C is a diagram illustrating the first example embodiment.

FIG. 3D is a diagram illustrating the first example embodiment.

FIG. 3E is a diagram illustrating the first example embodiment.

FIG. 3F is a diagram illustrating the first example embodiment.

FIG. 3G is a diagram illustrating the first example embodiment.

FIG. 4 is a diagram illustrating another configuration example accordingto a first example embodiment.

FIG. 5 is a diagram illustrating a configuration example according to asecond example embodiment.

FIG. 6 is a diagram illustrating the second example embodiment.

FIG. 7A is a diagram illustrating the second example embodiment.

FIG. 7B is a diagram illustrating the second example embodiment.

FIG. 8A is a diagram illustrating the second example embodiment.

FIG. 8B is a diagram illustrating the second example embodiment.

FIG. 8C is a diagram illustrating the second example embodiment.

FIG. 8D is a diagram illustrating the second example embodiment.

FIG. 8E is a diagram illustrating the second example embodiment.

FIG. 9 is a flowchart for illustrating an operation example according tothe second example embodiment.

FIG. 10 is a flowchart for illustrating an operation example accordingto the second example embodiment.

FIG. 11 is a flowchart for illustrating an operation example accordingto the second example embodiment.

FIG. 12 is a diagram illustrating a configuration example according to athird example embodiment.

FIG. 13A is a diagram illustrating the third example embodiment.

FIG. 13B is a diagram illustrating the third example embodiment.

FIG. 14 is a flowchart for illustrating an operation example accordingto the third example embodiment.

FIG. 15 is a diagram illustrating an example according to a fourthexample embodiment.

DESCRIPTION OF EMBODIMENTS

A system according to one of embodiments of the present inventionincludes an encoding module (for example, 20 in FIG. 2) that convertsinput data such as stimulus information inputted from outside the systemto a batch of a plurality of elements and takes in the plurality ofelements as internal information, an integration module (for example, 30in FIG. 2) that converts an individual element to an interpretablerepresentation expression and integrates the representation expressionsas a vector, and a determination module (for example, 40 in FIG. 2) thatmaps the vector on a semantic space and determines a recognition andclassification group. By updating a representation expression and arecognition and classification group in coordination, a representationexpression that could not be expressed for new input can be acquired,and unknown stimuli distributed on the semantic space can be made toconverge to a recognition and classification group which isdiscriminable from one or more known groups.

The system, which is provided with the integration module (30), inaddition to the encoding module (20) and the determination module (40),can autonomously acquire a recognition and classification group and arepresentation expression to enhance discrimination ability, whilegradually increasing the number of recognition and classificationgroups.

Example Embodiment 1

The following describes a first example embodiment with reference todrawings. FIG. 2 illustrates an example of a system configurationaccording to an example embodiment of the present invention. Aclassification system 1 according to the first example embodimentincludes an input apparatus 10, an encoding module 20, an integrationmodule 30, and a determination module 40. These modules may have such anapparatus configuration in which they are provided in a single unit. Inthis case, the classification system 1 is configured as a classificationapparatus (classifier). Alternatively, the input apparatus 10, theencoding module 20, the integration module 30, and the determinationmodule 40 may be respectively implemented as individual apparatuses andmay be communicatively connected with each other via communicationmeans. Still alternatively, for example, a combination of theintegration module 30 and the determination module 40 may be implementedon a server or the like, and the input apparatus 10 and the encodingmodule 20 may be implemented on a network node or the like that iscommunicatively connected with the server.

In FIG. 2, while each arrow indicates a direction of information/asignal between units, this is only an example for illustrating a featureaccording to the present example embodiment and signal transmission andreception is not limited to be unidirectional. Namely, a configurationin which a signal is transmitted and received bi-directionally betweenunits (a handshake or transmission and reception of a control signal,etc. between modules) is not excluded. The same holds true for FIGS. 3A,4, 5, and 12.

In FIG. 2, the encoding module 20 receives input data from the inputapparatus 10 that receives an external stimulus or the like and acquiresthe input data as internal information that can be processed inside theencoding module 20. Though not limited thereto, the input apparatus 10may be a sensor, a camera, or the like. For example, the sensor may be adevice configured to sense any one of vibration information,current/voltage, temperature information, smell, biometric information,image information, acoustic information, etc. The input apparatus 10 mayinclude an Internet-of-things (IoT) device that includes wired orwireless communication means and may be communicatively connected withthe encoding module 20 via a network.

Alternatively, the input apparatus 10 may be an interface module or acontroller that reads and inputs sensing data or image data alreadystored in a storage apparatus such as a database.

The integration module 30 converts the acquired information to elementinformation needed for collation and generates an integrated vector.

The determination module 40 collates the integrated vector with arepresentative vector of a semantic model (a representative vector of aclassified group) and outputs a recognition result (a group ID) 44corresponding to the external stimulus.

More specifically, the encoding module 20 includes an elementclassification part 23, an element classification model learning part21, and a storage apparatus 22 to store an element classification model.

The element classification model learning part 21 creates a featuretargeted for extraction from the input data (element data) and stores anelement classification model in the storage apparatus 22. The elementclassification model learning part 21 may perform learning with a groupID determined by the determination module 40 as an input.

Based on the element classification model stored in the storageapparatus 22, the element classification part 23 extracts and classifiesa feature of the input data per element data and outputs classificationinformation, which is a group of the classified element.

The integration module 30 includes an element estimation model learningpart 31, an element estimation part 33, and a storage apparatus 32 tostore an element estimation model.

The element estimation model learning part 31 creates an elementexpression based on the classification information outputted from theelement classification part 23 of the encoding module 20 and the inputdata (element data) and stores the element expression in the storageapparatus 32 as an element estimation model. The element estimationmodel learning part 31 may perform learning with a group ID determinedby the determination module 40 as an input.

The element estimation part 33 receives the classification informationoutputted from the element classification part 23 of the encoding module20, converts the classification information to an element expressionused for collation with reference to the element estimation model storedin the storage apparatus 32, and outputs the element expression to thedetermination module 40.

The determination module 40 includes a determination (decision-making)part 42, a group updating and generation part 41, and a storageapparatus 43 to store a semantic model.

The group updating and generation part 41 creates a recognition andclassification group from integrated information and stores therecognition and classification group in the storage apparatus 43 as asemantic model.

The determination part 42 collates the classification informationoutputted from the element estimation part 33 of the integration module30 with a learned semantic model stored in the storage apparatus 43 anddetermines a recognition and classification group which is classified.

FIGS. 3A to 3G illustrate an operation principle of the classificationsystem 1 in FIG. 2 in view of data (information) flow in theclassification system 1. FIG. 3A illustrates a configuration in FIG. 2and data (information). FIGS. 3A to 3G only illustrate operationexamples according to the first example embodiment, and therefore, datastructure, content, expression, etc. are, as a matter of course, notlimited to the examples in FIGS. 3A to 3G. D=(d1, d2, d3, d4, d5) to theencoding module 20. FIG. 3B illustrates an example of the data=(d1, d2,d3, d4, d5). In FIG. 3B, a vertical axis indicates, for example,magnitude of data, and a horizontal axis indicates, for example,location, time or another parameter, and is classified into regions of afixed size.

The element classification part 23 receives input data D=(d1, d2, d3,d4, d5), extracts a feature of the input data based on an elementclassification model stored in the storage apparatus 22, and outputsclassification information, which is an element classification group,X=(x1, x2). In the classification information X, x1 represents a region(number from front) where a peak of a waveform of data in FIG. 3Bresides. The division information X=(x1, x2)=(2, 5) corresponding to d1represents that the data d1 has a peak in a region 2 and a value of thepeak is 5. This division information X=(x1, x2) corresponds to a featureextracted from the data (see FIG. 3C). The element x1 is subjected toclassification based on an element classification model corresponding toelement data (type) ϕ={ϕ1, ϕ2, ϕ3, . . . }. The element x2 represents amagnitude of the peak. The element x2 is subjected to classificationbased on an element classification model corresponding to element data(type) ψ={ψ1, ψ2, . . . }.

The element classification model learning part 21 creates an elementclassification modes based on the element data ϕ={ϕ2, ϕ1, ϕ1, ϕ2, ϕ3}and ψ={ψ2, ψ1, ψ1, ψ2, ψ3 } and stores the element classification modelsin the storage apparatus 22. The element data ϕ is constituted bylocation (region) of a peak (hill) of a waveform of the input data, and{ϕ2, ϕ1, ϕ1, ϕ2, ϕ3} represents an ensemble of types (peak locations).The element data ψ is constituted by a peak value, etc., and {ψ2, ψ1,ψ1, ψ2, ψ3 } represents an ensemble of peak values corresponding to thelocations of the peaks.

The division information X=(x1, x2) (see FIG. 3C) outputted from theelement classification part 23 is supplied to the element estimationpart 33 and the element estimation model learning part 31 of theintegration module 30. The element estimation model learning part 31 maybe configured to receive the division information X=(x1, x2) whenlearning an element estimation model.

Based on the element estimation model stored in the storage apparatus32, the element estimation part 33 converts the division information toan element expression vector Z=(z1, z2) and outputs the elementexpression vector Z to the determination module 40. FIG. 3Dschematically illustrates one of the element estimation models stored inthe storage apparatus 32. While FIG. 3D illustrates only p (ϕ|x1=1) asan example for convenience of formulation of the drawing, the elementestimation model stored in the storage apparatus 32 may includeprobability distributions other than p(ϕ|x1=1), such as p(ϕ|x1=2) andp(ϕ|x2=5), may be stored as an element estimation model.

In FIG. 3D, p(ϕ|x1=1) is a probability (conditional probability) withwhich the element data (type) is ϕ when x1=1 is given in the case of theinputted classification information X=(x1, x2). Likewise, p(ϕ|x2=1) is aprobability (conditional probability) with which the element data (type)is ψ when x2=1 is given. In the element estimation model in FIG. 3D,element data ϕion the horizontal axis of p(ϕ|x1=1) is ϕ1, ϕ2, and ϕ3. Inthe case of the element estimation model in FIG. 3D, the probabilityp(ϕ|x1=1) has a distribution in which the highest value is around ϕ3.

Based on the element estimation model (probability p(ϕ|x1)) illustratedas an example in FIG. 3D, the element estimation part 33 performssampling of type ϕk(k=1, . . . , 3) and sets a sampled value as anelement z1 of a vector Z. Though not limited thereto, for example, theelement estimation part 33 finds a most probable ϕ(≈ϕ3) with respect tox1=1 in FIG. 3D, by using Naive Bayes or the like (the probabilityp(ϕ|x1=1) takes the largest vale) (z1 will be a value close to 3 whenassuming that ϕ1, ϕ2, and ϕ3 on the horizontal axis ϕ in FIG. 3D arequantified as 1, 2, and 3, respectively).

Likewise, based on the element estimation model (probability p(ψ|x2)),the element estimation part 33 performs sampling of type ψk(k=1, 2, . .. ) and determines an obtained value as the element z2 of the vector Z.In this operation, for example, the element estimation part 33 may findthe most probable ψ with respect to x2=5 (the largest probabilityp(ψ|x2=5) takes the largest value).

FIG. 3E illustrates the correspondence between the classificationinformation X=(x1, x2) inputted to the element estimation part 33 andthe expression vector (collation vector) Z=(z1, z2) which is obtained bysampling X=(x1, x2) using the probabilities p(ϕ|x1) and p(ψ|x2). Theelement estimation part 33 may have a Bayesian Attractor configuration.

As illustrated in FIG. 3F, the determination part (decision making) 42determines a group (group ID) 44 to which the vector Z belongs, based ona distance between the vector Z=(z1, z2) (FIG. 3E) and a representativevector rg1=(g11, g12) of an individual group stored in the storageapparatus 43 as a semantic model. In the example in FIG. 3F, in 2dimension space (ϕ, ψ), a group corresponding to the shortest distancebetween the vector Z (indicated by a dot) and the representative vectorrg¹ (indicated by an arrow) is determined. If the distance to therepresentative vector rg1=(g11, g12) of the group is a predeterminedthreshold or more, the group updating and generation part 41 maygenerate a new group.

In the example in FIG. 3G, in a case of x1=8, ϕ4 is newly added to theelement data (type) ϕ (ϕ becomes {ϕ1, ϕ2, ϕ3, ϕ4}). Consequently, avalue (frequency) is high at ϕ4 (the probability p(ϕ4|x1=8) increases).FIG. 3G illustrates a situation in which grouping corresponding to ϕ4 isperformed and a classification accuracy is improved.

When the probability p(ϕ|x1) does not exceed a predetermined threshold,the element estimation model learning part 31 may add the new type ϕ4,for example, based on a notification (for example, a group ID) from thedetermination part 42 (the same holds true for the probability p(ψ|x2)).

The group updating and generation part 41 may receive a correct answerlabel G={G1, G1, G2, G1, G3} (a correct answer label of a group) fromthe input apparatus 10 and generate the representative vector of aclassification group stored as a semantic model in the storage apparatus43. The group updating and generation part 41 may update therepresentative vector of a classification group stored as a semanticmodel in the storage apparatus 43 based on a vector Z from thedetermination part 42.

FIG. 4 illustrates another configuration example according to the firstexample embodiment. As illustrated in FIG. 4, the integration module 30includes an element integration part 34 that generates a vector(integrated vector) by integrating additional data 13 inputted from theinput apparatus 10 with an expression vector Z outputted from theelement estimation part 33 and outputs the vector to the determinationmodule 40. For example, the additional data 13 is attribute informationof the input data 11 and is inputted in association with the input data11. If the input data 11 is image data including an animal as a subject,as will be described below, the additional data 13 may be attributeinformation (digital signal) such as animal call or the like(feature(s)).

Example Embodiment 2

FIG. 5 illustrates a configuration according to a second exampleembodiment of the present invention. As illustrated in FIG. 5, anencoding module 20 includes an element data generation part 25, inaddition to the configuration in FIG. 2.

The element data generation part 25 converts input data inputted from aninput apparatus 10 to data needed for classification by referring to anelement model stored in a storage apparatus 24. The conversion method isstored in advance as an element model in the storage apparatus 24.

The element data generation part 25 may generate a plurality of kinds ofdata and output each of the generated data to an element classificationpart 23.

Though not limited thereto, for example, the data conversion performedby the element data generation part 25 is at least one of the following:

-   binarization conversion,-   frequency conversion,-   color space conversion,-   object extraction, and-   conversion using various signal processing filters.

The element classification part 23 extracts a feature per generatedelement data, classifies the feature, and outputs data which is acollection of element classification.

An element classification model learning part 21 creates a feature to beextracted per generated data and stores the feature as an elementclassification model in a storage apparatus 22. As the classificationlearning, supervised learning using a correct answer label 14 may beperformed. Alternatively, unsupervised learning may be performed basedon self-organizing maps (SOM).

An element estimation part 33 of an integration module 30 receives theelement classification set outputted from the element classificationpart 23 of the encoding module 20 and converts the elementclassification set to a learned interpretable representation expressionvector.

The classification result has a distribution from a true value due to anobservation error. In addition, designing is not always made in such amanner that the classification result is expressed as beinginterpretable by humans. This is especially prominent in a case wherethe classification learning is performed based on unsupervised learning.

The element estimation part 33 converts the element classification onthe divided elements to a stable representation expression that can beinterpreted by humans. The element estimation part 33 performs theconversion by referring to an element classification model per element.The following example will be made, assuming that, with regard to anelement k, the inputted classification result is Xk and the typeobtained as a result of the conversion is ϕk.

A probability pk(ϕk|Xk), with which conversion to ϕk is performed whenXk is given, is stored as an element classification model. The elementestimation part 33 performs sampling of the type ϕk based on thisprobability and sets the sampled value as an element of the expressionvector Z. The expression vector Z is also referred to as arepresentation expression vector.

An element estimation model learning part 31 generates an elementclassification model by using learning data in advance and stores theelement classification models in a storage apparatus 32. [0059]

Assuming that there are i kinds of representation that can be obtainedwith regard to the element k, and the i-th type is ϕki, pk(ϕk|Xk) isconstituted in accordance with frequencies of respective types. In theexample in FIG. 6, three kinds (i=3) of representation can be obtainedwith regard to the element k. For example, there is created aconditional probability pki(ϕki|Xk) with which learning data is ϕki whenclassification information Xk is given by the element classificationpart 23. The individual ϕki is a discrete value defined on acorresponding element axis ϕk, and pk(ϕk|Xk) is created as a probabilitywith which the probability pki(ϕki|Xk) is approximated by using acorresponding value. For example, pk(ϕk|Xk) can be formed as a sum ofstandard distributions (standard normal distributions) in which aprobability value or data numbers is used as a parameter.

Regarding the learning data used in a prior learning, the value of thetype ϕki is defined based on similarity or the like between(among) thetypes ϕki. It is noted that the representation expression of a singleelement is not limited to a one-dimensional expression. Therepresentation expression can be extended to an N-dimensional (N is aninteger of 2 or more).

When the element estimation is performed, if the probability pk(ϕk|Xk)or the probability pki(ϕki|Xk) of any of the types ϕki does not exceed apredetermined threshold, the element estimation model learning part 31can add a new type ϕki autonomously or in accordance with an instructionfrom outside. In FIG. 7A, all the probabilities pki(ϕki|Xk) of types ϕ1kto ϕ3k are below a threshold. Thus, a new type ϕ4K is added, asillustrated in FIG. 7B. Namely, the number of types is increased by one.

The element estimation model learning part 31 autonomously adds a newtype when the probability of an element other than k is high at aspecific type. Namely, there is a case where, while an element cannot beexpressed by existing representations when viewed only on a k axis, theelement can be expressed by an existing representation when viewed onanother axis.

When learning the probability of the newly added type ϕki, the elementestimation model learning part 31 uses only Xk corresponding to theinput data that may converge to an existing type on another axis. Asidefrom that, the element estimation model learning part 31 may beinstructed to modify a probability so that an existing type will be acorrect answer, without adding a type. A value given to a new type maybe determined based on a similarity to an existing type. Alternatively,a value given to a new type may be determined in accordance with aninstruction given from outside or a rule such as increment.

Though not limited thereto, an example in which, for example, texture,color, and shape may be used as an example of an element data will bedescribed. The corresponding element classification are classificationby frequency spectra, color spectra, and extracted objects.

For example, as learning data, there are prepared the following:

texture type ϕ1 is {flat, striped, reticulated, etc.},

color type ϕ2 is {yellow, gray, brown, etc.},

shape type ϕ3 is {long neck, short neck, medium neck, . . . } and soforth.

When the input data is an image of a tiger and an image of a giraffe,

the tiger is expressed as (striped, yellow, short neck), and

the giraffe is expressed as (reticulated, yellow, long neck), forexample. While an example in which the input data is image has beendescribed, the input data is not, as a matter of course, limited toimage. While notation in which an index k (k=1, 2, 3, . . . ) is used todistinguish a type ϕk such as ϕ1, ϕ2(ϕ3), etc., ϕ, ψ(η), etc. will beused in place of ϕ1, ϕ2(ϕ3), etc. for ease of visibility in FIGS. 8B to8E, etc.

An element integration part 34 appends additional data 13 received as anew element from the input apparatus 10 to the expression vector Z andoutputs an integrated vector. Though not limited thereto, the additionaldata may be an attribute such as a call or smell which may be inputted,for example, in association with the input data D, for example (forexample, the input data 11 and the additional data 13 may be inputted asa pair).

A determination part (decision making module) 42 searches for arepresentative vector rg closest to the inputted integrated vector Z andoutputs a group to which the representative vector rg belongs as a groupID 44. A representative vector rg is a vector that represents arecognized classification group defined on a semantic space.

Regarding a closeness between vectors, for example, a distance isevaluated based on a weighted square sum. Assuming that a weight is wi,the integrated vector is zi, and rgi of a group g is a representativevector, a distance g is given as follows. In the following formula, Σiis a summation operator. In this example, summation corresponding to thenumber of groups is performed.

Distance g=Σ _(i) wi*|zi−rgi| ²

When the distance between the integrated vector and any of therepresentative vectors is equal to or more than a predeterminedthreshold, it is decided that a group is an unknown group.

In the above distance g, the weight wi is a parameter for settingimportance of a specific element and is referred to as “selectiveattention”. The weight may simply be set statically based on a purposeof recognition or set from outside. Alternatively, the weight may be setdynamically, for example, based on a classification accuracy of theelement classification part 23.

The number of recognition and classification groups or therepresentative vectors are stored in a storage apparatus 43 as asemantic model.

A group updating and generation part 41 generates and updates arepresentative vector of a classification group based on the integratedvector and a correct answer label. The integrated vector may be acquiredfrom the determination part 42. The correct answer label 14 is acquiredfrom the input apparatus 10. For example, a representative vector is amedian value of vectors having the same label. The group updating andgeneration part 41 may update the representative vector of thedetermined group by using the integrated vector identified by thedetermination part 42.

The group updating and generation part 41 may perform the followingprocessing such as

-   updating the representative vector to be a median value of all the    past vectors belonging to the corresponding group,-   setting a certain window (a time window) and updating the    representative vector to be a median value of recently used vectors,    or-   determining the degree of contribution of updating or whether    updating has been performed, based on the classification accuracy of    the element classification part 23.

The group updating and generation part 41 may generate a new group byusing the integrated vector identified to be an unknown group by thedetermination part 42.

When a predetermined number of vectors identified to be unknown aredistributed within a specific radius on the semantic space, the groupupdating and generation part 41 generates a representative vector whichis a median value of the vectors and stores a new group in the storageapparatus 43 as a semantic model.

FIGS. 8A to 8E illustrate a specific example of data processing of thesystem according to the second example embodiment described withreference to FIG. 5. The following example assumes that the followingdata is set as element data in the element classification model asillustrated in FIG. 8E.

-   ϕ={striped, flat, reticulated}={ϕ1, ϕ2, ϕ3},-   ψ={black and white, gray, yellow}={ψ1, ψ2, ψ3},-   η={medium neck, short neck}={η1, η2}.

In FIG. 8A, image data (still images) of d1 (zebra), d2 (elephant), andd3 (giraffe) are inputted as input data D (81). The element datageneration part 25 generates element data (82) of texture, color, andneck ratio from each of the input data D={d1, d2, d3 }. The elementclassification part 23 classifies the feature of the element data basedon an element classification model (ϕ, ψ, η) and outputs classificationinformation X1=(x11, x12, x13), (x21, x22, x23), and (x31, x32, x33)(83) corresponding to the input data D{d1, d2, d3}. Each of the elements(x11, x12), (x21, x22), and (x31, x32) is a histogram, and each of theelements x13, x23, and x33 is a ratio, for example.

The element estimation part 33 receives the classification informationX=(x1, x2, x3) and outputs an expression vector Z=(z1, z2, z3) (84) inFIG. 8A based on the element estimation model. In this example, theelement integration part 34 outputs the expression vector Z as anintegrated vector to the determination part 42 without appendingadditional data.

As illustrated in FIG. 8B, the determination part 42 determines a groupID to which the vector Z belongs, based on the distance between thevector Z from the integration module 30 and the representative vector rgof respective groups G¹, G², etc.

The element estimation model learning part 31 may be configured to add anew type η3 when the probabilities p(η|x3=2) of the types rel and η2 donot exceed a predetermined threshold. In FIG. 8C, both of theprobabilities p(η|x3=2) of the types η1 and η2 do not exceed apredetermined threshold. Thus, as illustrated in FIG. 8D, a new type 113(long neck) is added. In this way, it is made possible to performclassification based on the type η3 (long neck) and to increaseresolution.

Next, an operation according to the second example embodiment will bedescried.

First, an operation of determining a group will be described withreference to FIG. 9.

The element data generation part 25 converts the input data to dataneeded for classification (step S11).

For example, the element data generation part 25 performs frequencyconversion, color space conversion, or object extraction on an inputimage and outputs a frequency spectrum, a color spectrum, or an object.Frequency conversion and color space conversion may be performed on anextracted object.

The element classification part 23 performs feature extraction andclassification for each item of generated data and outputs an elementclassification aggregate data having each classification result as anelement (step S12).

For example, the element classification part 23 generates a dataaggregate having classification probabilities by using a neural network,a support vector machine (SVM), or the like, about the individualfrequency spectrum or color spectrum.

Regarding an object, the element classification part 23 extracts asub-region of the object and generates coordinate values for each of oneor more regions. The element classification part 23 refers to theelement classification model for the number of classifications and thenumber of regions.

The element estimation part 33 converts the inputted data aggregate to arepresentation expression vector (step S13).

It is assumed that the element is designated by k (k is defined as anyone of values 1 to 3 corresponding to frequency, color, and object).Each of the element classification is converted to an element (texture,color, shape) of a corresponding representative vector. Each type thatthe representation can take is already learned. For example, the textureis {flat, striped, reticulated}, the color is {yellow, gray, brown}, andthe shape is {vertically long, horizontally long, square}.

An element k of the representation expression vector is generated basedon a conditional probability when a classified element Xk is given. Whena type that can be taken is ϕki, the probability is given aspki(ϕki|Xk).

The following example assumes that ϕ11=1, ϕ12=2, and ϕ13=3, when k=1(texture) (flat, striped, and reticulated will be denoted by 1, 2, and3, respectively).

A probability p1(ϕ1|Xk) that gives a texture type is constituted on thistexture axis, and sampling is performed based on this probability.

The element integration part 34 adds additional data (additionalinformation) 13 as a new element to the representation expression vectorand outputs an integrated vector (step S14). The additional informationis an attribute value related to an input and information derived fromother modality such as call and/or smell may also be integrated.

The determination part 42 searches for a representative vector closestto the inputted integrated vector and outputs a group to which therepresentative vector belongs as a group ID (step S15).

The closeness between vectors is evaluated based on a weighted squaresum, where the weight may be given from outside depending on a purpose.

The group updating and generation part 41 updates a searchedrepresentative vector by using the inputted integrated vector (stepS16).

In step S15 in FIG. 9, the determination part 42 searches for arepresentative vector closest to the inputted integrated vector. If adistance between the integrated vector and any one of the representativevectors is greater than or equal to a threshold, the determination part42 determines that the inputted integrated vector belongs to an unknowngroup and outputs an unknown group ID.

In this case, in step S16, when a predetermined number of integratedvectors having an unknown group ID are distributed within a specificradius on the semantic space, the group updating and generation part 41generates a representative vector which is a median value of theintegrated vectors and generates a new group.

Next, an operation of updating the element axis (ϕk) described withreference to FIG. 7 will be described with reference to FIG. 10. StepsS21 to S24 are the same as steps Sll to S 14 in FIG. 9, respectively.

There are cases in which, while a group is not constituted whenevaluation is performed on all the element axes, the condition ofgenerating a group is satisfied when evaluation is performed on only afixed number of axes. In such cases, the determination part 42temporarily generates a group ID and feeds back the group ID to theelement estimation model learning part 31 and the element classificationmodel learning part 21 (step S25).

For example, there is such a case where, while convergence to anexisting type can be achieved on the color axis and the object axis,convergence to an existing type cannot be achieved on the texture axis(k=1). Namely, there is a case where a probability p1i of an existingtype does not exceed a threshold.

On reception of a group ID that has been temporarily generated and fedback, the element estimation model learning part 31 can add a new type(ϕ14=4: fourth item of type ϕk(k=1), a value thereof is set to 4) (stepS26). The element estimation model learning part 31 may createp14(ϕ14|X1) by using the input element classification X1 that convergeto the same type on the color axis and the object axis and update aprobability pl of the texture axis. A value obtained by incrementing themaximum value of the existing type is used as a value given to the newtype. The value of the added type can be given from outside, and whetherlearning is performed or not can be instructed.

On reception of the group ID that has been fed back and temporarilygenerated, the element classification model learning part 21 updates theelement classification model on the axis on which convergence cannot beachieved by using the temporarily generated group ID as a correct answerlabel (step S27). The correct answer label can be given from outside ofthe apparatus via the input apparatus 10, and whether learning isperformed or not can be instructed. The input apparatus 10 may be acommunication interface (a receiver of a transceiver) that receives datafrom the outside, or an input interface including a keyboard, or thelike.

Next, an operation of performing prior learning according to the secondexample embodiment will be described with reference with FIG. 11. StepsS31, S33, S35, S36, and S37 are the same as steps S11, S12, S13, S14,and S15 in FIG. 9, respectively.

The element classification model learning part 21 learns an elementclassification model by using data generated by the element datageneration part 25 (step S32). When the classification is performed byusing a neural network, a weight of the neural network may be learned byusing a correct answer label.

The element estimation model learning part 31 creates pki(ϕki|Xk) andpk(ϕk|Xk) from classification information Xk generated by the elementclassification part 23 and the correct answer label (type ϕki) (stepS34).

The group updating and generation part 41 generates a median value ofthe integrated vectors that have the same correct answer label (groupID) and that have been generated by the element integration part 34 as arepresentative vector and generates a group (step S38).

Whether learning data ends is determined (step S39). Steps S31 to S38are repeated until there is no learning data.

In FIG. 5, when the additional data 13 is not appended, the elementintegration part 34 may be removed, and the output of the elementestimation part 33 may be outputted to the determination part 42.

Third Example Embodiment

FIG. 12 illustrates a configuration according to a third exampleembodiment of the present invention. As illustrated in FIG. 12, anelement data generation part 25 includes a division estimation section251 and an element division section 252. The division estimation section251 receives, for example, input data such as continuous image data andextracts a feature(s) for dividing the input data.

The extracted division feature(s) are stored in a storage apparatus 24as an element model. The division estimation section 251 refers to theelement model in the storage apparatus 24 when performing the divisionestimation.

The element division section 252 divides and outputs the input databased on the division feature(s). An encoding module 20 may furtherinclude an element model learning part 26. The element model learningpart 26 can create an element model from the input data and the divisioninformation.

Though not limited thereto, an example is assumed where

-   continuous image data of an animal, as input data 11;-   information on joint coordinates, as division information 15; and-   a kind of animal, as a correct answer label 14.

An animal can roughly be approximated by a multi-joint rigid body basedon its skeleton. For example, an animal can be expressed as an object inwhich rigid body parts such as a face, a neck, a trunk, legs, and a tailare connected at joints. If the joint coordinates are known, individualparts can be divided from each other.

The correspondence between joint coordinates and parts are given inadvance. In this case, the division estimation section 251 extracts thejoint coordinates as a division feature.

The element model learning part 26 creates an element model based onimage data, which is the input data, and information on the jointcoordinates, which corresponds to the division information 15.

For example, the element model learning part 26 may receive image data,perform learning using, for example, deep learning technology with theinformation on the joint coordinates as training data, and store alearned network model in the storage apparatus 24 as an element model.

When learning is not performed, the element model learning part 26generates joint coordinates having a large error for the image datareceived. The element model learning part 26 creates a model in such amanner that the error would be minimized.

The element division section 252 divides the input data 11 (image) intoelement images based on the division information (joint coordinates).

For example, the element images are images corresponding to a face, aneck, a trunk, legs, a tail, etc.

An element classification part 23 classifies the element images obtainedby dividing the input data 11. For example, the element classificationpart 23 may perform the classification by using deep learningtechnology. A learned model by an element classification model learningmodule may be used as a network model. Alternatively, a learned modellearned by using a general image may be used. Output of the elementclassification part 23 may be, for example, a data aggregate havingclassification probabilities as elements thereof.

An element classification model learning part 21 can build an elementclassification model based on an element image and a correct answerlabel.

For example, the element classification model learning part 21 receivesthe element images, performs learning by using deep learning technologyon a per-element basis (per face, neck, trunk, leg, tail) by using partlabels (for example, a striped leg, a reticulated trunk, etc.) astraining data, and stores the learned network model, as elementclassification model.

An element estimation model learning part 31 may adaptively updateprobabilities pki(ϕki|Xk) and pk(ϕk|Xk), each time data is inputted.Regarding an estimation result obtained by the element estimation part33, a probability for a specific type is expected to increase gradually,if plural items of known data of the same kind are continuously inputted(see FIGS. 13A and 13B). FIG. 13B illustrates that a probability for aspecific type 42k increases as compared with that in FIG. 13A.

For example, a probability for a specific type increases in such a casewhere a video of the same tiger is inputted. No result may be outputtedwhile the probability pk(ϕk|Xk) is below a threshold in any region. Arepresentation expression vector may be outputted when the probabilitypk(ϕk|Xk) exceeds the threshold. Regarding an element estimation part33, even when plural items of data of the same kind are inputtedcontinuously and stored, if the probability pk(ϕk|Xk) or the probabilitypki(ϕki|Xk) of any type ϕki does not exceed a threshold, an elementestimation model learning module can add a type or perform updating sothat convergence to an existing type is achieved.

An element integration part 34 can integrate an expression vector oftexture, color, shape, etc. with a representation expression vector ofthe element image. It is noted that the above example has been describedassuming that the input data is image, but the input data is not, as amatter of course, limited to image.

Next, an operation according to the third example embodiment will bedescribed with reference to FIG. 14.

The following describes an operation example in which adaptive learningis performed. The division estimation section 251 receives image dataand extracts joint coordinates (step S41). For example, the divisionestimation section 251 estimates joint coordinates by using deeplearning technology.

The element division section 252 divides the input image into elementimages, each of which includes a part including joints at both ends,based on the joint coordinates (step S42). The correspondence betweenthe joint coordinates and the parts is given in advance.

The element classification part 23 classifies each of the dividedelement images (step S43). For example, the element classification part23 estimates element classification by using deep learning technology.

The element estimation model learning part 31 updates probabilitiespki(ϕki|Xk) and pk(ϕk|Xk), each time data is inputted (step S44).

The element estimation model learning part 31 determines whether theprobability pk(ϕk|Xk) exceeds a predetermined threshold (step S45).

If the probability pk(ϕk|Xk) exceeds the threshold (Yes in step S45),the element estimation part 33 generates a representation expressionvector by using the probability pk(ϕk|Xk) and outputs the representationexpression vector to the element integration part 34 (step S46). If theprobability pk(ϕk|Xk) does not exceed the threshold, whether pluralitems of data of the same kind have been accumulated is determined (stepS50). If plural items of data of the same kind have not been accumulated(No in step S50), the processing returns to step S41.

If the probability pk(ϕk|Xk) does not exceed the threshold in anyregion, even when plural items of data of the same kind are continuouslyinputted (Yes in step S50), the element estimation model learning part31 adds a type (step S51), and the processing returns to step S41.

The element integration part 34 can integrate a representationexpression vector of texture, color, shape, etc. with a representationexpression vector of the element image (step S46).

Steps S47 and S48 are the same as steps S15 and S16 in FIG. 9,respectively.

Next, whether there is no learning data or not is determined (step S49).Steps S41 to S48 (also steps S50 and S51 as needed) are repeated untilthere is no learning data.

In FIG. 12, in a case where additional data 13 is not appended, theelement integration part 34 may be removed, and the output of theelement estimation part 33 may be outputted to a determination part 42.

According to the example embodiments, input data is divided intocollective elements, a division element (divided element) is convertedto an interpretable expression, and an expression vector having theinterpretable expression as an element is grouped on a semantic space.Operation on the expression(s) of the division element(s) and thegroup(s) on the semantic space are performed in a coordinated manner.

As a result, it is made possible to enhance stimulus discriminationability and to newly acquire a recognition and classification group.

Since a division element has a relatively simple feature with respect toan overall environmental stimulus, coordination with another axisachieves autonomous acquisition of an expression of a division element.Evaluation of combination of the expressions of division elements cancontribute to improvement of discriminability. By converting a divisionelement to an expression of a type value additionally acquired,observation of autonomous formation of a group is made possible, andinterference from outside is be made possible. By continuously modifyingelement expressions, more new groups can be acquired from a small numberof groups.

An individual one of the means (an individual one of the processingparts) according to the above example embodiments can be configured byhardware such as an integrated circuit (IC) and/or software. Forexample, an individual one of the means described above is configured bya computer. FIG. 15 illustrates a hardware configuration example of acomputer apparatus (classification apparatus) 300 as the computer. Thecomputer apparatus 300 includes a processor 301, an interface 302, and amemory 303, which are connected to each other via a bus 304. Theinterface 302 is used by data input-output means for data exchange. Forexample, a network device, a file device, or a sensor device isconnected to the interface 302. The processor 301 inputs and outputsvarious kinds of information via the interface 302. The processor 301executes a program stored in the memory 303. For example, the memory 303includes a main memory that holds a program executed by the processor301. The program stored in the memory 303 includes instructions(instruction codes) processed by the processor 301, to realize executionof processing of the individual means. The processor 301 may include amemory that holds instructions to be executed.

The classification apparatuses according to the example embodiments(including encoding modules 20, integration modules 30, anddetermination modules 40) may be realized by causing a correspondingprocessor 301 to execute a program for corresponding processing.Alternatively, the encoding module 20, the integration module 30, andthe determination module 40 may be realized respectively by the computerapparatuses 300 in FIG. 15.

The program executed by the processor 301 may be stored in any ofvarious types of non-transitory computer-readable medium and supplied toa computer. Examples of the non-transitory computer-readable mediuminclude various types of tangible storage medium. For example, thenon-transitory computer-readable medium is a magnetic storage medium(for example, a flexible disk, a magnetic tape, or a hard disk drive), amagneto-optical storage medium (for example, a magneto-optical disk), aCD-ROM (read-only memory), a CD-R, a CD-R/W, or a semiconductor memory(for example, a mask ROM, a programmable ROM (PROM), an erasable PROM(EPROM), a flash ROM, or a random access memory (RAM)). Alternatively,the program may be supplied to a computer via any of various types oftransitory computer-readable medium. Examples of the transitorycomputer-readable medium includes an electrical signal, an opticalsignal, and an electromagnetic wave. The transitory computer-readablemedium may supply the program to a computer via a wired communicationpath such as an electrical wire or an optical fiber or a wirelesscommunication path.

In FIG. 2, FIG. 3A, FIG. 4, FIG. 5, and FIG. 12, the input apparatus 10and the encoding module 20 may be implemented on a terminal, and theintegration module 30 and the determination module 40 may be implementedon a server apparatus connected to communicate with the terminal. Inthis case, the server apparatus may be configured as a server thatprovides a cloud service.

While examples in which image information, etc. is classified have beendescribed in the second and third example embodiments, the presentinvention is applicable to various kinds of apparatuses and systems,each of which extracts a feature from a signal (information) acquired bya sensing device, etc. and classify the feature.

The disclosure of the above PTL 1 is incorporated herein by referencethereto. Variations and adjustments of the example embodiment andexample are possible within the scope of the overall disclosure(including the claims) of the present invention and based on the basictechnical concept of the present invention. Various combinations andselections of various disclosed elements (including the elements in theclaims, example embodiment, examples, drawings, etc.) are possiblewithin the scope of the claims of the present invention. Namely, thepresent invention of course includes various variations andmodifications that could be made by those skilled in the art accordingto the overall disclosure including the claims and the technicalconcept.

REFERENCE SIGNS LIST

-   1 classification system (classification apparatus)-   10 input apparatus-   11 input data-   12 element data-   13 additional data-   14 correct answer label-   15 division information-   20 encoding module-   21 element classification model learning part-   22 storage apparatus (element classification model)-   23 element classification part-   24 storage apparatus (element model)-   25 element data generation part-   26 element model learning part-   30 integration module-   31 element estimation model learning part-   32 storage apparatus (element estimation model)-   33 element estimation part-   34 element integration part-   40 determination module-   41 group updating and generation part-   42 determination part-   43 storage apparatus (semantic model)-   44 group ID-   81 input data (image data)-   82 element data-   83 classification information-   84 expression vector-   101 input apparatus-   102 encoding module-   103 determination module-   251 division estimation section-   252 element division section-   300 computer apparatus-   301 processor-   302 interface-   303 memory-   304 bus-   1011 input data-   1021 feature extraction part-   1031 matching part-   1032 group ID

What is claimed is:
 1. A classification apparatus, comprising: aprocessor; and a memory storing program instructions executable by theprocessor, wherein the processor is configured to execute: an encodingprocess that performs an element classification process that extracts afeature of input data and outputs classification information based on anelement classification model stored in a first storage unit; anintegration process that performs an element estimation process thatreceives the classification information and converts the classificationinformation to a collation vector based on an element estimation modelstored in a second storage unit; and a determination process thatdetermines a group to which collation vector belongs by collating thecollation vector with a representative vector of an individual groupstored as a semantic model in a third storage unit to output a group IDof the group as a classification result.
 2. The classification apparatusaccording to claim 1, wherein the determination process calculates adistance between the collation vector and the representative vectorlearned as the semantic model to determine, when the distance is equalto or less than a predetermined value, a group ID to which therepresentative vector belongs, and wherein the determination processfurther performs a group updating and generation process that constructthe group from the collation vector and stores the group as a semanticmodel in the third storage unit.
 3. The classification apparatusaccording to claim 1, wherein the integration process further performsan element estimation model learning process that generates the elementestimation model based on the classification information and inputtedelement data, and wherein the element estimation model learning processlearns the element estimation model by using, as input, a group IDdetermined by the determination process.
 4. The classification apparatusaccording to claim 3, wherein the integration process further performsan element integration process that generates an integrated vector byappending additional information inputted in association with the inputdata to the vector generated by the element estimation part and outputsthe integrated vector to the determination process as the collationvector.
 5. The classification apparatus according to claim 1, whereinthe encoding process further performs an element classification modellearning process that generates the element classification model basedon inputted element data.
 6. The classification apparatus according toclaim 5, wherein the encoding process further performs an element datageneration process that receives the input data and converts the inputdata to element data needed for classification by the elementclassification process.
 7. The classification apparatus according toclaim 6, wherein the element data generation process performs: adivision estimation process that receives the input data and extracts afeature used for dividing the input data based on an element modelstored in a fourth storage unit; and an element division process thatdivides the input data based on the division feature and outputs thedivided input data for supply to the element classification process. 8.A classification method performed by a computer, the method comprising:an encoding process that includes performing an element classificationprocess that extracts a feature of input data and outputs classificationinformation based on an element classification model stored in a firststorage unit; an integration process that includes receiving theclassification information and converting the classification informationto a collation vector based on an element estimation model stored in asecond storage unit; and a determination process that includesdetermining a group to which the collation vector belongs by collatingthe collation vector with a representative vector of an individual groupstored as a semantic model in a third storage unit and outputting agroup ID of the group as a classification result.
 9. The classificationmethod according to claim 8, wherein the determination process includes:calculating a distance between the collation vector and therepresentative vector learned as the semantic model and determining,when the distance is equal to or less than a predetermined value, agroup ID to which the representative vector belongs; and constructingthe group from the collation vector and storing the group as a semanticmodel in the third storage unit.
 10. The classification method accordingto claim 8, wherein the integration process includes: receiving theclassification information and converting the classification informationto an expression vector based on the element estimation model; andperforming an element estimation model learning process that generatesthe element estimation model based on the classification information andinputted element data, and wherein the element estimation model learningprocess learns the element estimation model by using as input a group IDdetermined in the determination process.
 11. The classification methodaccording to claim 10, wherein the integration process includesgenerating an integrated vector by appending additional informationinputted in association with the input data to a vector generated basedon the classification information and the element estimation model andsupplying the integrated vector to the determination process as thecollation vector.
 12. The classification method according to claim 8,wherein the encoding process includes performing an elementclassification model learning process that generates the elementclassification model based on inputted element data.
 13. Theclassification method according to claim 12, wherein the encodingprocess further includes performing an element data generation processthat receives the input data and converts the input data to element dataneeded to create classified information.
 14. The classification methodaccording to claim 13, wherein the element data generation processincludes: performing a division estimation process that receives theinput data and extracts a feature used for dividing the input data basedon an element model stored in a fourth storage unit; and performing anelement division process that divides the input data based on thedivision feature and outputs the divided input data for supply to theelement classification process.
 15. A non-transitory computer-readablemedium storing a program causing a computer to execute: an encodingprocess that includes an element classification process that extracts afeature of input data and outputs classification information based on anelement classification model stored in a first storage unit; anintegration process that includes an element estimation process thatreceives the classification information and converts the classificationinformation to a collation vector based on an element estimation modelstored in a second storage unit; and a determination process thatdetermines a group to which the collation vector belongs by collatingthe collation vector with a representative vector of an individual groupstored as a semantic model in a third storage unit and outputs a groupID of the group as a classification result.
 16. The non-transitorycomputer-readable medium according to claim 15, wherein thedetermination process further includes: a process that calculates adistance between the collation vector and the representative vectorlearned as the semantic model and determines, when the distance is equalto or less than a predetermined value, a group ID to which therepresentative vector belongs; and a group updating and generationprocess that constructs the group from the collation vector and storesthe group as a semantic model in the third storage unit.
 17. Thenon-transitory computer-readable medium according to claim 15, whereinthe integration process further includes an element estimation modellearning process that generates the element estimation model based onthe classification information and inputted element data and learns theelement estimation model by using a group ID determined in thedetermination process as input.
 18. The non-transitory computer-readablemedium according to claim 17, wherein the integration process furtherincludes an element integration process that generates an integratedvector by appending additional information inputted in association withthe input data to the vector generated in the element estimation processand outputs the integrated vector to the determination process as thecollation vector.
 19. The non-transitory computer-readable mediumaccording to claim 15, wherein the encoding process further includes anelement classification model learning process that generates the elementclassification model based on inputted element data.
 20. Thenon-transitory computer-readable medium according to claim 19, whereinthe encoding process further includes an element data generation processthat receives the input data and converts the input data to element dataneeded for classification in the element classification process. 21.(canceled)